Overview

Dataset statistics

Number of variables44
Number of observations62660
Missing cells523797
Missing cells (%)19.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 MiB
Average record size in memory352.0 B

Variable types

NUM19
CAT15
BOOL9
UNSUPPORTED1

Warnings

CountryName has a high cardinality: 185 distinct values High cardinality
CountryCode has a high cardinality: 185 distinct values High cardinality
RegionName has a high cardinality: 56 distinct values High cardinality
RegionCode has a high cardinality: 56 distinct values High cardinality
StringencyIndex is highly correlated with C3_Cancel public events and 7 other fieldsHigh correlation
C3_Cancel public events is highly correlated with StringencyIndex and 1 other fieldsHigh correlation
StringencyIndexForDisplay is highly correlated with C3_Cancel public events and 7 other fieldsHigh correlation
StringencyLegacyIndex is highly correlated with StringencyIndex and 6 other fieldsHigh correlation
StringencyLegacyIndexForDisplay is highly correlated with StringencyIndex and 6 other fieldsHigh correlation
GovernmentResponseIndex is highly correlated with StringencyIndex and 6 other fieldsHigh correlation
GovernmentResponseIndexForDisplay is highly correlated with StringencyIndex and 6 other fieldsHigh correlation
ContainmentHealthIndex is highly correlated with StringencyIndex and 6 other fieldsHigh correlation
ContainmentHealthIndexForDisplay is highly correlated with StringencyIndex and 6 other fieldsHigh correlation
EconomicSupportIndex is highly correlated with E2_Debt/contract relief and 1 other fieldsHigh correlation
E2_Debt/contract relief is highly correlated with EconomicSupportIndex and 1 other fieldsHigh correlation
EconomicSupportIndexForDisplay is highly correlated with E2_Debt/contract relief and 1 other fieldsHigh correlation
RegionCode is highly correlated with RegionNameHigh correlation
RegionName is highly correlated with RegionCodeHigh correlation
RegionName has 48100 (76.8%) missing values Missing
RegionCode has 48100 (76.8%) missing values Missing
C1_School closing has 1811 (2.9%) missing values Missing
C1_Flag has 23326 (37.2%) missing values Missing
C2_Workplace closing has 1874 (3.0%) missing values Missing
C2_Flag has 25378 (40.5%) missing values Missing
C3_Cancel public events has 1829 (2.9%) missing values Missing
C3_Flag has 22289 (35.6%) missing values Missing
C4_Restrictions on gatherings has 1819 (2.9%) missing values Missing
C4_Flag has 25218 (40.2%) missing values Missing
C5_Close public transport has 1790 (2.9%) missing values Missing
C5_Flag has 39037 (62.3%) missing values Missing
C6_Stay at home requirements has 1852 (3.0%) missing values Missing
C6_Flag has 29563 (47.2%) missing values Missing
C7_Restrictions on internal movement has 1809 (2.9%) missing values Missing
C7_Flag has 30075 (48.0%) missing values Missing
C8_International travel controls has 1844 (2.9%) missing values Missing
E1_Income support has 2206 (3.5%) missing values Missing
E1_Flag has 31700 (50.6%) missing values Missing
E2_Debt/contract relief has 3116 (5.0%) missing values Missing
E3_Fiscal measures has 13502 (21.5%) missing values Missing
E4_International support has 13314 (21.2%) missing values Missing
H1_Public information campaigns has 2148 (3.4%) missing values Missing
H1_Flag has 13103 (20.9%) missing values Missing
H2_Testing policy has 2355 (3.8%) missing values Missing
H3_Contact tracing has 2355 (3.8%) missing values Missing
H4_Emergency investment in healthcare has 14029 (22.4%) missing values Missing
H5_Investment in vaccines has 13601 (21.7%) missing values Missing
M1_Wildcard has 62660 (100.0%) missing values Missing
ConfirmedCases has 12608 (20.1%) missing values Missing
ConfirmedDeaths has 12734 (20.3%) missing values Missing
StringencyIndex has 1889 (3.0%) missing values Missing
StringencyIndexForDisplay has 902 (1.4%) missing values Missing
StringencyLegacyIndex has 1889 (3.0%) missing values Missing
StringencyLegacyIndexForDisplay has 902 (1.4%) missing values Missing
GovernmentResponseIndex has 2516 (4.0%) missing values Missing
GovernmentResponseIndexForDisplay has 1527 (2.4%) missing values Missing
ContainmentHealthIndex has 2340 (3.7%) missing values Missing
ContainmentHealthIndexForDisplay has 1359 (2.2%) missing values Missing
EconomicSupportIndex has 3143 (5.0%) missing values Missing
EconomicSupportIndexForDisplay has 2185 (3.5%) missing values Missing
E3_Fiscal measures is highly skewed (γ1 = 111.0973872) Skewed
E4_International support is highly skewed (γ1 = 221.5222898) Skewed
H4_Emergency investment in healthcare is highly skewed (γ1 = 161.0341444) Skewed
H5_Investment in vaccines is highly skewed (γ1 = 89.92087778) Skewed
RegionName is uniformly distributed Uniform
RegionCode is uniformly distributed Uniform
M1_Wildcard is an unsupported type, check if it needs cleaning or further analysis Unsupported
C4_Restrictions on gatherings has 23399 (37.3%) zeros Zeros
C8_International travel controls has 12544 (20.0%) zeros Zeros
E3_Fiscal measures has 48575 (77.5%) zeros Zeros
E4_International support has 49258 (78.6%) zeros Zeros
H4_Emergency investment in healthcare has 48214 (76.9%) zeros Zeros
H5_Investment in vaccines has 48927 (78.1%) zeros Zeros
ConfirmedCases has 5015 (8.0%) zeros Zeros
ConfirmedDeaths has 11948 (19.1%) zeros Zeros
StringencyIndex has 8850 (14.1%) zeros Zeros
StringencyIndexForDisplay has 8850 (14.1%) zeros Zeros
StringencyLegacyIndex has 8850 (14.1%) zeros Zeros
StringencyLegacyIndexForDisplay has 8850 (14.1%) zeros Zeros
GovernmentResponseIndex has 7637 (12.2%) zeros Zeros
GovernmentResponseIndexForDisplay has 7637 (12.2%) zeros Zeros
ContainmentHealthIndex has 7677 (12.3%) zeros Zeros
ContainmentHealthIndexForDisplay has 7677 (12.3%) zeros Zeros
EconomicSupportIndex has 23077 (36.8%) zeros Zeros
EconomicSupportIndexForDisplay has 23213 (37.0%) zeros Zeros

Reproduction

Analysis started2020-10-02 20:43:30.422500
Analysis finished2020-10-02 20:45:03.152912
Duration1 minute and 32.73 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CountryName
Categorical

HIGH CARDINALITY

Distinct185
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size489.5 KiB
United States
13780 
United Kingdom
 
1300
South Sudan
 
260
United Arab Emirates
 
260
Mauritius
 
260
Other values (180)
46800 
ValueCountFrequency (%) 
United States1378022.0%
 
United Kingdom13002.1%
 
South Sudan2600.4%
 
United Arab Emirates2600.4%
 
Mauritius2600.4%
 
Italy2600.4%
 
Tunisia2600.4%
 
Bermuda2600.4%
 
Bangladesh2600.4%
 
South Africa2600.4%
 
Other values (175)4550072.6%
 
2020-10-03T06:45:03.304405image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:03.512230image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length8
Mean length9.468879668
Min length4

CountryCode
Categorical

HIGH CARDINALITY

Distinct185
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size489.5 KiB
USA
13780 
GBR
 
1300
BHR
 
260
MEX
 
260
ALB
 
260
Other values (180)
46800 
ValueCountFrequency (%) 
USA1378022.0%
 
GBR13002.1%
 
BHR2600.4%
 
MEX2600.4%
 
ALB2600.4%
 
BMU2600.4%
 
ITA2600.4%
 
DOM2600.4%
 
CYM2600.4%
 
GIB2600.4%
 
Other values (175)4550072.6%
 
2020-10-03T06:45:03.751524image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:03.943266image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

RegionName
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct56
Distinct (%)0.4%
Missing48100
Missing (%)76.8%
Memory size489.5 KiB
Wisconsin
 
260
Louisiana
 
260
North Dakota
 
260
Colorado
 
260
Indiana
 
260
Other values (51)
13260 
ValueCountFrequency (%) 
Wisconsin2600.4%
 
Louisiana2600.4%
 
North Dakota2600.4%
 
Colorado2600.4%
 
Indiana2600.4%
 
Kentucky2600.4%
 
West Virginia2600.4%
 
New Jersey2600.4%
 
North Carolina2600.4%
 
England2600.4%
 
Other values (46)1196019.1%
 
(Missing)4810076.8%
 
2020-10-03T06:45:04.126246image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:04.316292image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length3
Mean length4.315352697
Min length3

RegionCode
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct56
Distinct (%)0.4%
Missing48100
Missing (%)76.8%
Memory size489.5 KiB
US_DE
 
260
US_AR
 
260
US_NY
 
260
US_TX
 
260
US_WA
 
260
Other values (51)
13260 
ValueCountFrequency (%) 
US_DE2600.4%
 
US_AR2600.4%
 
US_NY2600.4%
 
US_TX2600.4%
 
US_WA2600.4%
 
US_OR2600.4%
 
US_MT2600.4%
 
US_WY2600.4%
 
US_KS2600.4%
 
UK_WAL2600.4%
 
Other values (46)1196019.1%
 
(Missing)4810076.8%
 
2020-10-03T06:45:04.544275image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:04.755653image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.481327801
Min length3

Date
Real number (ℝ≥0)

Distinct260
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200494.54
Minimum20200101
Maximum20200916
Zeros0
Zeros (%)0.0%
Memory size489.5 KiB
2020-10-03T06:45:04.955198image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum20200101
5-th percentile20200113.95
Q120200305.75
median20200509.5
Q320200713.25
95-th percentile20200903.05
Maximum20200916
Range815
Interquartile range (IQR)407.5

Descriptive statistics

Standard deviation246.6868853
Coefficient of variation (CV)1.221192307e-05
Kurtosis-1.198407293
Mean20200494.54
Median Absolute Deviation (MAD)204
Skewness0.01219764366
Sum1.265762988e+12
Variance60854.41939
MonotocityNot monotonic
2020-10-03T06:45:05.165593image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
202008242410.4%
 
202009082410.4%
 
202007162410.4%
 
202006202410.4%
 
202005242410.4%
 
202004282410.4%
 
202002042410.4%
 
202001082410.4%
 
202009092410.4%
 
202008132410.4%
 
Other values (250)6025096.2%
 
ValueCountFrequency (%) 
202001012410.4%
 
202001022410.4%
 
202001032410.4%
 
202001042410.4%
 
202001052410.4%
 
ValueCountFrequency (%) 
202009162410.4%
 
202009152410.4%
 
202009142410.4%
 
202009132410.4%
 
202009122410.4%
 

C1_School closing
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing1811
Missing (%)2.9%
Memory size489.5 KiB
3
29841 
0
21515 
2
8046 
1
 
1447
ValueCountFrequency (%) 
32984147.6%
 
02151534.3%
 
2804612.8%
 
114472.3%
 
(Missing)18112.9%
 
2020-10-03T06:45:05.394238image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:05.516546image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:05.646184image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

C1_Flag
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing23326
Missing (%)37.2%
Memory size489.5 KiB
1
32736 
0
6598 
(Missing)
23326 
ValueCountFrequency (%) 
13273652.2%
 
0659810.5%
 
(Missing)2332637.2%
 
2020-10-03T06:45:05.742594image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

C2_Workplace closing
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing1874
Missing (%)3.0%
Memory size489.5 KiB
0
23504 
2
21140 
3
8417 
1
7725 
ValueCountFrequency (%) 
02350437.5%
 
22114033.7%
 
3841713.4%
 
1772512.3%
 
(Missing)18743.0%
 
2020-10-03T06:45:05.869047image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:05.989084image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:06.122424image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

C2_Flag
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing25378
Missing (%)40.5%
Memory size489.5 KiB
1
30001 
0
7281 
(Missing)
25378 
ValueCountFrequency (%) 
13000147.9%
 
0728111.6%
 
(Missing)2537840.5%
 
2020-10-03T06:45:06.223830image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

C3_Cancel public events
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing1829
Missing (%)2.9%
Memory size489.5 KiB
2
32595 
0
20460 
1
7776 
ValueCountFrequency (%) 
23259552.0%
 
02046032.7%
 
1777612.4%
 
(Missing)18292.9%
 
2020-10-03T06:45:06.341176image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:06.436883image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:06.556445image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

C3_Flag
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing22289
Missing (%)35.6%
Memory size489.5 KiB
1
35502 
0
4869 
(Missing)
22289 
ValueCountFrequency (%) 
13550256.7%
 
048697.8%
 
(Missing)2228935.6%
 
2020-10-03T06:45:06.657030image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

C4_Restrictions on gatherings
Real number (ℝ≥0)

MISSING
ZEROS

Distinct5
Distinct (%)< 0.1%
Missing1819
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean2.039989481
Minimum0
Maximum4
Zeros23399
Zeros (%)37.3%
Memory size489.5 KiB
2020-10-03T06:45:06.758889image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q34
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.728211337
Coefficient of variation (CV)0.8471667884
Kurtosis-1.732308885
Mean2.039989481
Median Absolute Deviation (MAD)1
Skewness-0.15985484
Sum124115
Variance2.986714425
MonotocityNot monotonic
2020-10-03T06:45:06.902338image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
02339937.3%
 
41826129.1%
 
31393922.2%
 
240126.4%
 
112302.0%
 
(Missing)18192.9%
 
ValueCountFrequency (%) 
02339937.3%
 
112302.0%
 
240126.4%
 
31393922.2%
 
41826129.1%
 
ValueCountFrequency (%) 
41826129.1%
 
31393922.2%
 
240126.4%
 
112302.0%
 
02339937.3%
 

C4_Flag
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing25218
Missing (%)40.2%
Memory size489.5 KiB
1
31661 
0
5781 
(Missing)
25218 
ValueCountFrequency (%) 
13166150.5%
 
057819.2%
 
(Missing)2521840.2%
 
2020-10-03T06:45:07.034126image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

C5_Close public transport
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing1790
Missing (%)2.9%
Memory size489.5 KiB
0
37247 
1
14898 
2
8725 
ValueCountFrequency (%) 
03724759.4%
 
11489823.8%
 
2872513.9%
 
(Missing)17902.9%
 
2020-10-03T06:45:07.171614image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:07.297066image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:07.430657image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

C5_Flag
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing39037
Missing (%)62.3%
Memory size489.5 KiB
1
15632 
0
7991 
(Missing)
39037 
ValueCountFrequency (%) 
11563224.9%
 
0799112.8%
 
(Missing)3903762.3%
 
2020-10-03T06:45:07.559499image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Distinct4
Distinct (%)< 0.1%
Missing1852
Missing (%)3.0%
Memory size489.5 KiB
0
27711 
1
16047 
2
14729 
3
 
2321
ValueCountFrequency (%) 
02771144.2%
 
11604725.6%
 
21472923.5%
 
323213.7%
 
(Missing)18523.0%
 
2020-10-03T06:45:07.698392image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:07.805346image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:07.934402image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

C6_Flag
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing29563
Missing (%)47.2%
Memory size489.5 KiB
1
25686 
0
7411 
(Missing)
29563 
ValueCountFrequency (%) 
12568641.0%
 
0741111.8%
 
(Missing)2956347.2%
 
2020-10-03T06:45:08.037714image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing1809
Missing (%)2.9%
Memory size489.5 KiB
0
28266 
2
19662 
1
12923 
ValueCountFrequency (%) 
02826645.1%
 
21966231.4%
 
11292320.6%
 
(Missing)18092.9%
 
2020-10-03T06:45:08.176788image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:08.298417image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:08.421747image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

C7_Flag
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing30075
Missing (%)48.0%
Memory size489.5 KiB
1
22523 
0
10062 
(Missing)
30075 
ValueCountFrequency (%) 
12252335.9%
 
01006216.1%
 
(Missing)3007548.0%
 
2020-10-03T06:45:08.520740image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

C8_International travel controls
Real number (ℝ≥0)

MISSING
ZEROS

Distinct5
Distinct (%)< 0.1%
Missing1844
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean2.422520389
Minimum0
Maximum4
Zeros12544
Zeros (%)20.0%
Memory size489.5 KiB
2020-10-03T06:45:08.619043image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.465192159
Coefficient of variation (CV)0.6048213941
Kurtosis-1.028603581
Mean2.422520389
Median Absolute Deviation (MAD)1
Skewness-0.6228006186
Sum147328
Variance2.146788063
MonotocityNot monotonic
2020-10-03T06:45:08.765097image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
32075533.1%
 
41675226.7%
 
01254420.0%
 
2729011.6%
 
134755.5%
 
(Missing)18442.9%
 
ValueCountFrequency (%) 
01254420.0%
 
134755.5%
 
2729011.6%
 
32075533.1%
 
41675226.7%
 
ValueCountFrequency (%) 
41675226.7%
 
32075533.1%
 
2729011.6%
 
134755.5%
 
01254420.0%
 

E1_Income support
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing2206
Missing (%)3.5%
Memory size489.5 KiB
0
29494 
2
17938 
1
13022 
ValueCountFrequency (%) 
02949447.1%
 
21793828.6%
 
11302220.8%
 
(Missing)22063.5%
 
2020-10-03T06:45:08.961316image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:09.081211image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:09.220583image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

E1_Flag
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing31700
Missing (%)50.6%
Memory size489.5 KiB
0
17810 
1
13150 
(Missing)
31700 
ValueCountFrequency (%) 
01781028.4%
 
11315021.0%
 
(Missing)3170050.6%
 
2020-10-03T06:45:09.324562image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

E2_Debt/contract relief
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing3116
Missing (%)5.0%
Memory size489.5 KiB
0
26463 
2
17220 
1
15861 
ValueCountFrequency (%) 
02646342.2%
 
21722027.5%
 
11586125.3%
 
(Missing)31165.0%
 
2020-10-03T06:45:09.525445image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:09.691664image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:09.831621image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

E3_Fiscal measures
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct521
Distinct (%)1.1%
Missing13502
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean205268856.8
Minimum0
Maximum1.9576e+12
Zeros48575
Zeros (%)77.5%
Memory size489.5 KiB
2020-10-03T06:45:10.006619image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.9576e+12
Range1.9576e+12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.258300326e+10
Coefficient of variation (CV)61.30010882
Kurtosis14647.82032
Mean205268856.8
Median Absolute Deviation (MAD)0
Skewness111.0973872
Sum1.009060646e+13
Variance1.58331971e+20
MonotocityNot monotonic
2020-10-03T06:45:10.212566image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04857577.5%
 
311< 0.1%
 
100000007< 0.1%
 
2000000005< 0.1%
 
200000004< 0.1%
 
160000004< 0.1%
 
20000004< 0.1%
 
10000000004< 0.1%
 
5800000003< 0.1%
 
500000003< 0.1%
 
Other values (511)5380.9%
 
(Missing)1350221.5%
 
ValueCountFrequency (%) 
04857577.5%
 
0.751< 0.1%
 
311< 0.1%
 
401< 0.1%
 
1521< 0.1%
 
ValueCountFrequency (%) 
1.9576e+121< 0.1%
 
1.192572826e+121< 0.1%
 
9.90863e+111< 0.1%
 
7.092759225e+111< 0.1%
 
4.526563438e+111< 0.1%
 

E4_International support
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct75
Distinct (%)0.2%
Missing13314
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean18194267.81
Minimum0
Maximum8.343530518e+11
Zeros49258
Zeros (%)78.6%
Memory size489.5 KiB
2020-10-03T06:45:10.428081image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8.343530518e+11
Range8.343530518e+11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3759553616
Coefficient of variation (CV)206.6339605
Kurtosis49158.74476
Mean18194267.81
Median Absolute Deviation (MAD)0
Skewness221.5222898
Sum8.978143395e+11
Variance1.413424339e+19
MonotocityNot monotonic
2020-10-03T06:45:10.649829image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04925878.6%
 
50000005< 0.1%
 
100000003< 0.1%
 
92474262< 0.1%
 
1869201302< 0.1%
 
10000002< 0.1%
 
300000002< 0.1%
 
1584294442< 0.1%
 
200000002< 0.1%
 
400000002< 0.1%
 
Other values (65)660.1%
 
(Missing)1331421.2%
 
ValueCountFrequency (%) 
04925878.6%
 
0.561< 0.1%
 
31< 0.1%
 
501< 0.1%
 
150001< 0.1%
 
ValueCountFrequency (%) 
8.343530518e+111< 0.1%
 
3.4e+101< 0.1%
 
1.2e+101< 0.1%
 
27000000001< 0.1%
 
24580123691< 0.1%
 
Distinct3
Distinct (%)< 0.1%
Missing2148
Missing (%)3.4%
Memory size489.5 KiB
2
44680 
0
10955 
1
4877 
ValueCountFrequency (%) 
24468071.3%
 
01095517.5%
 
148777.8%
 
(Missing)21483.4%
 
2020-10-03T06:45:10.861814image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:10.988390image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:11.126207image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

H1_Flag
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing13103
Missing (%)20.9%
Memory size489.5 KiB
1
49123 
0
 
434
(Missing)
13103 
ValueCountFrequency (%) 
14912378.4%
 
04340.7%
 
(Missing)1310320.9%
 
2020-10-03T06:45:11.230378image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

H2_Testing policy
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing2355
Missing (%)3.8%
Memory size489.5 KiB
1
22480 
2
16200 
0
12266 
3
9359 
ValueCountFrequency (%) 
12248035.9%
 
21620025.9%
 
01226619.6%
 
3935914.9%
 
(Missing)23553.8%
 
2020-10-03T06:45:11.358603image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:11.474783image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:11.599806image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

H3_Contact tracing
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing2355
Missing (%)3.8%
Memory size489.5 KiB
2
23548 
1
21079 
0
15678 
ValueCountFrequency (%) 
22354837.6%
 
12107933.6%
 
01567825.0%
 
(Missing)23553.8%
 
2020-10-03T06:45:11.774331image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-03T06:45:11.890467image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:12.009037image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

H4_Emergency investment in healthcare
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct310
Distinct (%)0.6%
Missing14029
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean13141758.34
Minimum0
Maximum2.424e+11
Zeros48214
Zeros (%)76.9%
Memory size489.5 KiB
2020-10-03T06:45:12.196397image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.424e+11
Range2.424e+11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1260861397
Coefficient of variation (CV)95.94312758
Kurtosis29116.89301
Mean13141758.34
Median Absolute Deviation (MAD)0
Skewness161.0341444
Sum6.390968498e+11
Variance1.589771462e+18
MonotocityNot monotonic
2020-10-03T06:45:12.423185image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04821476.9%
 
35390.1%
 
56220< 0.1%
 
20.710< 0.1%
 
10000007< 0.1%
 
2000000005< 0.1%
 
20000004< 0.1%
 
1000000004< 0.1%
 
500000003< 0.1%
 
400000003< 0.1%
 
Other values (300)3220.5%
 
(Missing)1402922.4%
 
ValueCountFrequency (%) 
04821476.9%
 
0.011< 0.1%
 
0.031< 0.1%
 
13< 0.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
2.424e+111< 0.1%
 
1e+111< 0.1%
 
6.29977238e+101< 0.1%
 
5.7458e+101< 0.1%
 
1.935477804e+101< 0.1%
 

H5_Investment in vaccines
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct67
Distinct (%)0.1%
Missing13601
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean424488.5569
Minimum0
Maximum3435342491
Zeros48927
Zeros (%)78.1%
Memory size489.5 KiB
2020-10-03T06:45:12.692486image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3435342491
Range3435342491
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24452672.66
Coefficient of variation (CV)57.605022
Kurtosis10059.39627
Mean424488.5569
Median Absolute Deviation (MAD)0
Skewness89.92087778
Sum2.082498411e+10
Variance5.979332003e+14
MonotocityNot monotonic
2020-10-03T06:45:12.900607image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04892778.1%
 
1430.1%
 
19121< 0.1%
 
100000003< 0.1%
 
254591132.12< 0.1%
 
2989616.72< 0.1%
 
1565108251< 0.1%
 
108198301< 0.1%
 
26014287.821< 0.1%
 
8260000001< 0.1%
 
Other values (57)570.1%
 
(Missing)1360121.7%
 
ValueCountFrequency (%) 
04892778.1%
 
1430.1%
 
1.11< 0.1%
 
19121< 0.1%
 
2469611< 0.1%
 
ValueCountFrequency (%) 
34353424911< 0.1%
 
19500000001< 0.1%
 
16797400001< 0.1%
 
16000000001< 0.1%
 
13900000001< 0.1%
 

M1_Wildcard
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing62660
Missing (%)100.0%
Memory size489.7 KiB

ConfirmedCases
Real number (ℝ≥0)

MISSING
ZEROS

Distinct21185
Distinct (%)42.3%
Missing12608
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean48126.50677
Minimum0
Maximum6486108
Zeros5015
Zeros (%)8.0%
Memory size489.5 KiB
2020-10-03T06:45:13.169197image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q176
median1832
Q318778.25
95-th percentile195283.35
Maximum6486108
Range6486108
Interquartile range (IQR)18702.25

Descriptive statistics

Standard deviation263402.4291
Coefficient of variation (CV)5.473125867
Kurtosis262.677804
Mean48126.50677
Median Absolute Deviation (MAD)1832
Skewness14.74818189
Sum2408827917
Variance6.938083966e+10
MonotocityNot monotonic
2020-10-03T06:45:13.377451image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
050158.0%
 
17491.2%
 
34830.8%
 
23800.6%
 
82890.5%
 
112890.5%
 
132770.4%
 
182740.4%
 
102370.4%
 
52040.3%
 
Other values (21175)4185566.8%
 
(Missing)1260820.1%
 
ValueCountFrequency (%) 
050158.0%
 
17491.2%
 
23800.6%
 
34830.8%
 
41890.3%
 
ValueCountFrequency (%) 
64861081< 0.1%
 
64452881< 0.1%
 
63972271< 0.1%
 
63597201< 0.1%
 
63277931< 0.1%
 

ConfirmedDeaths
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6986
Distinct (%)14.0%
Missing12734
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean2104.534131
Minimum0
Maximum193701
Zeros11948
Zeros (%)19.1%
Memory size489.5 KiB
2020-10-03T06:45:14.207930image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median42
Q3507
95-th percentile8515.25
Maximum193701
Range193701
Interquartile range (IQR)506

Descriptive statistics

Standard deviation9823.484289
Coefficient of variation (CV)4.667771431
Kurtosis143.0807322
Mean2104.534131
Median Absolute Deviation (MAD)42
Skewness10.49547858
Sum105070971
Variance96500843.58
MonotocityNot monotonic
2020-10-03T06:45:14.417853image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01194819.1%
 
118693.0%
 
29541.5%
 
38131.3%
 
107301.2%
 
55880.9%
 
65630.9%
 
45550.9%
 
95210.8%
 
75070.8%
 
Other values (6976)3087849.3%
 
(Missing)1273420.3%
 
ValueCountFrequency (%) 
01194819.1%
 
118693.0%
 
29541.5%
 
38131.3%
 
45550.9%
 
ValueCountFrequency (%) 
1937011< 0.1%
 
1930161< 0.1%
 
1917891< 0.1%
 
1908151< 0.1%
 
1896791< 0.1%
 

StringencyIndex
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct179
Distinct (%)0.3%
Missing1889
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean47.82498248
Minimum0
Maximum100
Zeros8850
Zeros (%)14.1%
Memory size489.5 KiB
2020-10-03T06:45:14.644434image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.89
median56.48
Q375
95-th percentile90.74
Maximum100
Range100
Interquartile range (IQR)61.11

Descriptive statistics

Standard deviation31.70643319
Coefficient of variation (CV)0.6629680043
Kurtosis-1.345483028
Mean47.82498248
Median Absolute Deviation (MAD)24.08
Skewness-0.2840201
Sum2906372.01
Variance1005.297905
MonotocityNot monotonic
2020-10-03T06:45:14.888551image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0885014.1%
 
11.1124093.8%
 
5.5618422.9%
 
13.8912322.0%
 
19.4411161.8%
 
79.6310951.7%
 
80.569741.6%
 
66.679321.5%
 
57.419141.5%
 
77.788931.4%
 
Other values (169)4051464.7%
 
(Missing)18893.0%
 
ValueCountFrequency (%) 
0885014.1%
 
1.3923< 0.1%
 
1.853< 0.1%
 
2.788551.4%
 
5.5618422.9%
 
ValueCountFrequency (%) 
1004770.8%
 
98.151< 0.1%
 
97.22770.1%
 
96.37261.2%
 
95.3715< 0.1%
 

StringencyIndexForDisplay
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct179
Distinct (%)0.3%
Missing902
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean47.92461203
Minimum0
Maximum100
Zeros8850
Zeros (%)14.1%
Memory size489.5 KiB
2020-10-03T06:45:15.112212image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.89
median56.48
Q375
95-th percentile90.74
Maximum100
Range100
Interquartile range (IQR)61.11

Descriptive statistics

Standard deviation31.57119783
Coefficient of variation (CV)0.6587679376
Kurtosis-1.332826485
Mean47.92461203
Median Absolute Deviation (MAD)24.07
Skewness-0.2891562882
Sum2959728.19
Variance996.7405323
MonotocityNot monotonic
2020-10-03T06:45:15.330863image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0885014.1%
 
11.1124233.9%
 
5.5618422.9%
 
13.8912392.0%
 
19.4411351.8%
 
79.6310951.7%
 
80.5610001.6%
 
66.679411.5%
 
57.419261.5%
 
77.789071.4%
 
Other values (169)4140066.1%
 
(Missing)9021.4%
 
ValueCountFrequency (%) 
0885014.1%
 
1.3923< 0.1%
 
1.853< 0.1%
 
2.788551.4%
 
5.5618422.9%
 
ValueCountFrequency (%) 
1004770.8%
 
98.151< 0.1%
 
97.22770.1%
 
96.37321.2%
 
95.3715< 0.1%
 

StringencyLegacyIndex
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct171
Distinct (%)0.3%
Missing1889
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean53.4078888
Minimum0
Maximum100
Zeros8850
Zeros (%)14.1%
Memory size489.5 KiB
2020-10-03T06:45:15.546947image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.86
median65.48
Q380.95
95-th percentile95.24
Maximum100
Range100
Interquartile range (IQR)63.09

Descriptive statistics

Standard deviation33.2048891
Coefficient of variation (CV)0.6217225553
Kurtosis-1.262121593
Mean53.4078888
Median Absolute Deviation (MAD)21.42
Skewness-0.4719108617
Sum3245650.81
Variance1102.56466
MonotocityNot monotonic
2020-10-03T06:45:15.746968image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0885014.1%
 
91.6722613.6%
 
83.3321503.4%
 
88.118312.9%
 
96.4315142.4%
 
14.2914042.2%
 
78.5713852.2%
 
73.8113682.2%
 
10013622.2%
 
66.6712342.0%
 
Other values (161)3741259.7%
 
(Missing)18893.0%
 
ValueCountFrequency (%) 
0885014.1%
 
2.8623< 0.1%
 
3.577651.2%
 
4.76930.1%
 
7.149311.5%
 
ValueCountFrequency (%) 
10013622.2%
 
96.4315142.4%
 
95.246491.0%
 
93.572< 0.1%
 
92.866271.0%
 

StringencyLegacyIndexForDisplay
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct171
Distinct (%)0.3%
Missing902
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean53.52691311
Minimum0
Maximum100
Zeros8850
Zeros (%)14.1%
Memory size489.5 KiB
2020-10-03T06:45:15.951353image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.86
median65
Q380.95
95-th percentile95.24
Maximum100
Range100
Interquartile range (IQR)63.09

Descriptive statistics

Standard deviation33.04482844
Coefficient of variation (CV)0.6173497875
Kurtosis-1.244487057
Mean53.52691311
Median Absolute Deviation (MAD)21.9
Skewness-0.4793930708
Sum3305715.1
Variance1091.960686
MonotocityNot monotonic
2020-10-03T06:45:16.173600image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0885014.1%
 
91.6722683.6%
 
83.3321813.5%
 
88.118523.0%
 
96.4315272.4%
 
14.2914112.3%
 
73.8114102.3%
 
78.5713992.2%
 
10013622.2%
 
66.6712542.0%
 
Other values (161)3824461.0%
 
ValueCountFrequency (%) 
0885014.1%
 
2.8623< 0.1%
 
3.577651.2%
 
4.76930.1%
 
7.149311.5%
 
ValueCountFrequency (%) 
10013622.2%
 
96.4315272.4%
 
95.246491.0%
 
93.572< 0.1%
 
92.866291.0%
 

GovernmentResponseIndex
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct235
Distinct (%)0.4%
Missing2516
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean46.93394935
Minimum0
Maximum96.15
Zeros7637
Zeros (%)12.2%
Memory size489.5 KiB
2020-10-03T06:45:16.374741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.38
median57.37
Q369.95
95-th percentile82.69
Maximum96.15
Range96.15
Interquartile range (IQR)54.57

Descriptive statistics

Standard deviation28.86445146
Coefficient of variation (CV)0.6150015471
Kurtosis-1.240447217
Mean46.93394935
Median Absolute Deviation (MAD)17.63
Skewness-0.5010066693
Sum2822795.45
Variance833.1565582
MonotocityNot monotonic
2020-10-03T06:45:16.580722image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0763712.2%
 
10.2612011.9%
 
14.110611.7%
 
74.369311.5%
 
68.598981.4%
 
76.928811.4%
 
64.748371.3%
 
3.858341.3%
 
69.237991.3%
 
65.387781.2%
 
Other values (225)4428770.7%
 
(Missing)25164.0%
 
ValueCountFrequency (%) 
0763712.2%
 
0.9620< 0.1%
 
1.925590.9%
 
2.562970.5%
 
3.858341.3%
 
ValueCountFrequency (%) 
96.1529< 0.1%
 
94.8717< 0.1%
 
93.5914< 0.1%
 
92.9525< 0.1%
 
92.3118< 0.1%
 

GovernmentResponseIndexForDisplay
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct235
Distinct (%)0.4%
Missing1527
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean47.09060671
Minimum0
Maximum96.15
Zeros7637
Zeros (%)12.2%
Memory size489.5 KiB
2020-10-03T06:45:16.801178image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.03
median57.37
Q369.87
95-th percentile82.69
Maximum96.15
Range96.15
Interquartile range (IQR)53.84

Descriptive statistics

Standard deviation28.72293448
Coefficient of variation (CV)0.6099504018
Kurtosis-1.21780082
Mean47.09060671
Median Absolute Deviation (MAD)17.63
Skewness-0.5124810096
Sum2878790.06
Variance825.0069654
MonotocityNot monotonic
2020-10-03T06:45:17.012946image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0763712.2%
 
10.2612011.9%
 
14.110751.7%
 
74.369311.5%
 
76.929041.4%
 
68.598981.4%
 
64.748441.3%
 
3.858341.3%
 
65.388031.3%
 
69.237991.3%
 
Other values (225)4520772.1%
 
(Missing)15272.4%
 
ValueCountFrequency (%) 
0763712.2%
 
0.9620< 0.1%
 
1.925590.9%
 
2.562970.5%
 
3.858341.3%
 
ValueCountFrequency (%) 
96.1529< 0.1%
 
94.8717< 0.1%
 
93.5914< 0.1%
 
92.9525< 0.1%
 
92.3118< 0.1%
 

ContainmentHealthIndex
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct209
Distinct (%)0.3%
Missing2340
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean48.67370076
Minimum0
Maximum100
Zeros7677
Zeros (%)12.3%
Memory size489.5 KiB
2020-10-03T06:45:17.246282image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.18
median58.71
Q372.73
95-th percentile86.36
Maximum100
Range100
Interquartile range (IQR)54.55

Descriptive statistics

Standard deviation29.68497035
Coefficient of variation (CV)0.6098769949
Kurtosis-1.211280608
Mean48.67370076
Median Absolute Deviation (MAD)19.32
Skewness-0.4694781795
Sum2935997.63
Variance881.1974648
MonotocityNot monotonic
2020-10-03T06:45:17.454116image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0767712.3%
 
12.1212322.0%
 
16.6710721.7%
 
759771.6%
 
72.739601.5%
 
70.458931.4%
 
79.558261.3%
 
4.558221.3%
 
59.098131.3%
 
86.368111.3%
 
Other values (199)4423770.6%
 
(Missing)23403.7%
 
ValueCountFrequency (%) 
0767712.3%
 
1.1420< 0.1%
 
2.275450.9%
 
3.032970.5%
 
4.558221.3%
 
ValueCountFrequency (%) 
100450.1%
 
96.971410.2%
 
95.45360.1%
 
94.74< 0.1%
 
93.942990.5%
 

ContainmentHealthIndexForDisplay
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct209
Distinct (%)0.3%
Missing1359
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean48.81816186
Minimum0
Maximum100
Zeros7677
Zeros (%)12.3%
Memory size489.5 KiB
2020-10-03T06:45:17.657336image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.94
median58.71
Q372.73
95-th percentile85.61
Maximum100
Range100
Interquartile range (IQR)53.79

Descriptive statistics

Standard deviation29.54694904
Coefficient of variation (CV)0.6052450137
Kurtosis-1.191443589
Mean48.81816186
Median Absolute Deviation (MAD)18.56
Skewness-0.4788634024
Sum2992602.14
Variance873.0221977
MonotocityNot monotonic
2020-10-03T06:45:17.872064image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0767712.3%
 
12.1212322.0%
 
16.6710861.7%
 
759841.6%
 
72.739741.6%
 
70.459001.4%
 
79.558331.3%
 
4.558221.3%
 
59.098201.3%
 
76.528141.3%
 
Other values (199)4515972.1%
 
(Missing)13592.2%
 
ValueCountFrequency (%) 
0767712.3%
 
1.1420< 0.1%
 
2.275450.9%
 
3.032970.5%
 
4.558221.3%
 
ValueCountFrequency (%) 
100450.1%
 
96.971410.2%
 
95.45360.1%
 
94.74< 0.1%
 
93.942990.5%
 

EconomicSupportIndex
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing3143
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean37.59997144
Minimum0
Maximum100
Zeros23077
Zeros (%)36.8%
Memory size489.5 KiB
2020-10-03T06:45:18.052658image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.5
Q362.5
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation35.00595352
Coefficient of variation (CV)0.9310101093
Kurtosis-1.428356483
Mean37.59997144
Median Absolute Deviation (MAD)37.5
Skewness0.2315175826
Sum2237837.5
Variance1225.416782
MonotocityNot monotonic
2020-10-03T06:45:18.189413image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
02307736.8%
 
62.5823613.1%
 
5061549.8%
 
87.556319.0%
 
7552388.4%
 
2544007.0%
 
10029954.8%
 
37.529204.7%
 
12.58661.4%
 
(Missing)31435.0%
 
ValueCountFrequency (%) 
02307736.8%
 
12.58661.4%
 
2544007.0%
 
37.529204.7%
 
5061549.8%
 
ValueCountFrequency (%) 
10029954.8%
 
87.556319.0%
 
7552388.4%
 
62.5823613.1%
 
5061549.8%
 

EconomicSupportIndexForDisplay
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing2185
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean37.80177759
Minimum0
Maximum100
Zeros23213
Zeros (%)37.0%
Memory size489.5 KiB
2020-10-03T06:45:18.334511image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.5
Q362.5
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation34.96420009
Coefficient of variation (CV)0.9249353422
Kurtosis-1.428092845
Mean37.80177759
Median Absolute Deviation (MAD)37.5
Skewness0.2221339363
Sum2286062.5
Variance1222.495288
MonotocityNot monotonic
2020-10-03T06:45:18.481290image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
02321337.0%
 
62.5835313.3%
 
50633610.1%
 
87.556979.1%
 
7554218.7%
 
2545127.2%
 
10030604.9%
 
37.529924.8%
 
12.58911.4%
 
(Missing)21853.5%
 
ValueCountFrequency (%) 
02321337.0%
 
12.58911.4%
 
2545127.2%
 
37.529924.8%
 
50633610.1%
 
ValueCountFrequency (%) 
10030604.9%
 
87.556979.1%
 
7554218.7%
 
62.5835313.3%
 
50633610.1%
 

Interactions

2020-10-03T06:43:54.802245image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:54.981886image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:55.143284image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:55.320407image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:55.486565image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:55.635943image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:55.791471image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:55.949443image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:56.105027image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:56.262957image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:56.415467image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:56.608080image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:56.805076image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:56.978793image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:57.143588image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:57.295581image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:57.454524image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:57.642272image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:57.834269image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:57.998955image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:58.304810image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:58.517068image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:58.717287image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:58.876875image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:59.028527image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:59.287125image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:59.512396image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:59.688843image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:43:59.859133image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:00.021553image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:00.189521image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:00.348513image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:00.544753image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:00.738239image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:00.908439image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:01.103039image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:01.286555image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:01.453734image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:01.625581image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:01.834832image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:02.043832image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:02.254414image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:02.467830image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:02.630633image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:02.802988image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:03.025491image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:03.216444image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:03.407230image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:03.577035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:03.736397image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:03.894782image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:04.180927image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:04.356633image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:04.556220image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:04.751854image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:04.948938image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:05.135022image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:05.294136image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:05.450769image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:05.640968image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:05.845729image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:06.027015image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:06.235165image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:06.410175image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:06.583628image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:06.757401image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:06.964661image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:07.156621image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:07.331341image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:07.523508image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:07.752777image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:07.964841image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:08.149271image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:08.311159image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-03T06:44:08.646473image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:08.824294image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:08.969719image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:09.129712image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:09.288350image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:09.455891image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:09.603399image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:09.759037image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:09.924768image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:10.086758image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-03T06:44:10.726406image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:10.878221image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:11.199711image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:11.346961image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:11.483561image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:11.679173image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:11.872411image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:12.052554image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:12.264780image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:12.448835image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:12.629465image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:12.789600image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:12.972355image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:13.143035image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:13.308484image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:13.463468image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:13.654266image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:13.789774image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:13.940117image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:14.130205image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:14.284045image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:14.424794image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:14.595995image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:14.742357image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:14.889278image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:15.050887image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:15.223098image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:15.384094image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:15.575610image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:15.779739image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:15.971522image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:16.152259image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:16.317478image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:16.477511image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-03T06:44:50.931116image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:51.070270image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:51.211509image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:51.346880image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:51.493505image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:51.623960image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:51.753825image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:51.883163image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:52.013412image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:52.138915image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:52.265196image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:52.394590image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:52.593832image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:52.729461image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:52.858950image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:52.996782image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:53.134423image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:53.272536image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:53.422550image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:53.563062image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:53.698057image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:53.834621image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:53.962020image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:54.104645image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:54.265039image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:54.428439image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:54.611640image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:54.794712image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:54.958491image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:55.104013image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:55.234757image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:55.369055image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:55.503939image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-10-03T06:45:18.711604image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-03T06:45:19.293966image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-03T06:45:19.847263image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-03T06:45:20.552009image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-03T06:45:21.091854image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-03T06:44:56.014141image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:44:58.547566image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:00.509352image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-03T06:45:02.640080image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

CountryNameCountryCodeRegionNameRegionCodeDateC1_School closingC1_FlagC2_Workplace closingC2_FlagC3_Cancel public eventsC3_FlagC4_Restrictions on gatheringsC4_FlagC5_Close public transportC5_FlagC6_Stay at home requirementsC6_FlagC7_Restrictions on internal movementC7_FlagC8_International travel controlsE1_Income supportE1_FlagE2_Debt/contract reliefE3_Fiscal measuresE4_International supportH1_Public information campaignsH1_FlagH2_Testing policyH3_Contact tracingH4_Emergency investment in healthcareH5_Investment in vaccinesM1_WildcardConfirmedCasesConfirmedDeathsStringencyIndexStringencyIndexForDisplayStringencyLegacyIndexStringencyLegacyIndexForDisplayGovernmentResponseIndexGovernmentResponseIndexForDisplayContainmentHealthIndexContainmentHealthIndexForDisplayEconomicSupportIndexEconomicSupportIndexForDisplay
0ArubaABWNaNNaN202001010.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0
1ArubaABWNaNNaN202001020.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0
2ArubaABWNaNNaN202001030.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0
3ArubaABWNaNNaN202001040.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0
4ArubaABWNaNNaN202001050.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0
5ArubaABWNaNNaN202001060.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0
6ArubaABWNaNNaN202001070.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0
7ArubaABWNaNNaN202001080.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0
8ArubaABWNaNNaN202001090.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0
9ArubaABWNaNNaN202001100.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.0NaN0.00.0NaN0.00.00.00.0NaN0.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.00.0

Last rows

CountryNameCountryCodeRegionNameRegionCodeDateC1_School closingC1_FlagC2_Workplace closingC2_FlagC3_Cancel public eventsC3_FlagC4_Restrictions on gatheringsC4_FlagC5_Close public transportC5_FlagC6_Stay at home requirementsC6_FlagC7_Restrictions on internal movementC7_FlagC8_International travel controlsE1_Income supportE1_FlagE2_Debt/contract reliefE3_Fiscal measuresE4_International supportH1_Public information campaignsH1_FlagH2_Testing policyH3_Contact tracingH4_Emergency investment in healthcareH5_Investment in vaccinesM1_WildcardConfirmedCasesConfirmedDeathsStringencyIndexStringencyIndexForDisplayStringencyLegacyIndexStringencyLegacyIndexForDisplayGovernmentResponseIndexGovernmentResponseIndexForDisplayContainmentHealthIndexContainmentHealthIndexForDisplayEconomicSupportIndexEconomicSupportIndexForDisplay
62650ZimbabweZWENaNNaN202009073.01.01.01.02.01.03.01.01.01.02.01.02.01.04.01.01.00.00.00.02.01.01.01.00.00.0NaN6977.0207.080.5680.5688.1088.1066.0366.0373.4873.4825.025.0
62651ZimbabweZWENaNNaN202009083.01.01.01.02.01.03.01.01.01.02.01.02.01.04.01.01.00.00.00.02.01.01.01.00.00.0NaN7298.0210.080.5680.5688.1088.1066.0366.0373.4873.4825.025.0
62652ZimbabweZWENaNNaN202009093.01.01.01.02.01.03.01.01.01.02.01.02.01.04.01.01.00.00.00.02.01.01.01.00.00.0NaN7388.0218.080.5680.5688.1088.1066.0366.0373.4873.4825.025.0
62653ZimbabweZWENaNNaN202009103.01.01.01.02.01.03.01.01.01.02.01.02.01.04.01.01.00.00.00.02.01.01.01.00.00.0NaN7429.0222.080.5680.5688.1088.1066.0366.0373.4873.4825.025.0
62654ZimbabweZWENaNNaN202009113.01.01.01.02.01.03.01.01.01.02.01.02.01.04.01.01.00.00.00.02.01.01.01.00.00.0NaN7453.0222.080.5680.5688.1088.1066.0366.0373.4873.4825.025.0
62655ZimbabweZWENaNNaN202009123.01.01.01.02.01.03.01.01.01.02.01.02.01.04.01.01.00.00.00.02.01.01.01.00.00.0NaN7479.0224.080.5680.5688.1088.1066.0366.0373.4873.4825.025.0
62656ZimbabweZWENaNNaN202009133.01.01.01.02.01.03.01.01.01.02.01.02.01.04.01.01.00.00.00.02.01.01.01.00.00.0NaN7508.0224.080.5680.5688.1088.1066.0366.0373.4873.4825.025.0
62657ZimbabweZWENaNNaN202009142.01.01.01.02.01.03.01.01.01.02.01.02.01.04.01.01.00.00.00.02.01.01.01.00.00.0NaNNaNNaN76.8576.8584.5284.5263.4663.4670.4570.4525.025.0
62658ZimbabweZWENaNNaN202009152.01.01.01.02.01.03.01.01.01.02.01.02.01.04.01.01.00.00.00.02.01.01.01.00.00.0NaNNaNNaN76.8576.8584.5284.5263.4663.4670.4570.4525.025.0
62659ZimbabweZWENaNNaN20200916NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN76.85NaN84.52NaN63.46NaN70.45NaN25.0